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Automatic Feature Extraction Module for Change Detection in Al Ain, UAE: Analysis by Means of Multi-temporal Remote Sensing Data

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Abstract

Monitoring new changes in cities adjacent to dynamic sand dunes requires precise classifier technique. Unlike traditional techniques of supervised classification which use training sites, the integration of image transformation tasseled cap and automatic feature extraction module based on spectral signatures has provided to be sensitive and realistic techniques with time and cost effective. The proposed module was applied to Al Ain district, United Arab Emirates (UAE). The module consists of four steps in terms of segmentation, thresholding and clustering and computing attributes. The obtained greenness and classified maps were then enhanced by applying a 3 × 3 Sobel filter. The new changes were detected by combining the multi-temporal greenness and classification maps. Accuracy assessment and quantitative analysis were performed using confusion matrix and ground truthing. The results showed significant increasing in urban and agricultural areas from the year from 1990 to 2000 compared with the period of time from the year 2000 to 2006. The image difference showed that the vegetation and building classes had increased 7.58 and 20.28 km2 respectively. This study showed that image difference and fuzzy logic approach are the most sensitive techniques for detecting new changes in areas adjacent to dynamic sand dunes.

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References

  • Al Nuaimi, H. S. (2003). “Hydrogeological and geophysical studies on Al Jaww Plain, Al Ain Area, United Arab Emirates,” M.Sc Thesis, United Arab Emirates University, Al Ain, 2003, 159p.

  • Chen, J. P., Gong, C., He, R., & Shi, P. P. (2003). Land-use/landcover change detection using improved change-vector analysis. Photogrammetric Engineering & Remote Sensing, 69(9), 1303–1309.

    Google Scholar 

  • Civco, D. L., Hurd, J. D., Wilson, Song, M., & Zhang, Z. (2002). A comparison of land use and land cover change detection methods. In Proceedings of the 2002 Asprs Annual Convention (p. 12). Washington, DC.

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.

    Article  Google Scholar 

  • Congalton, R. G., Green, K., & Teply, J. (1993). Map** old growth forests on national forest and park lands in the Pacific Northwest from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 59(4), 529-535.

  • Dewindar, K. H. (2004). Detection of land use/land cover changes for the northern part of the Nile Delta (Burullus Region), Egypt. International Journal of Remote Sensing, 25(20), 4079–4089.

    Article  Google Scholar 

  • Fung, T., & Le, D. E. (1988). The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering & Remote Sensing, 54(10), 1449–1454.

    Google Scholar 

  • Gamanya, R., De Maeyer, P., & De Dapper, M. (2007). An automated satellite image classification design using object-oriented segmentation algorithms: a move towards standardization. Expert Systems with Applications, 32, 616–624.

    Article  Google Scholar 

  • Gamanya, R., De Maeyer, P., & De Dapper, M. (2009). Object-oriented change detection for the city of Harare, Zimbabwe. Expert Systems with Applications, 36(1), 571–588.

  • Ghosh, A., Shekhar, N. M., & Ghosh, S. (2010). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, Xxx(2010), Xxx–Xxx.

  • Gonzalez, R., & Woods, R. (1992). Digital image processing. Addison Wesley, pp. 414–428.

  • Jensen, J. (1996). Introductory digital image processing: A remote sensing perspective, Second Edition, Prentice Hall, 316p.

  • JICA, (Japanese International Cooperation Agency) (1996). The master plan study on the groundwater resources development for agriculture in the vicinity of Al Dhaid in the UAE, Jica International Cooperation Agency, (1996), Final Report.

  • Li, X., & Yeh, A. O. (1998). Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta. International Journal of Remote Sensing, 19(8), 1501–1518.

    Article  Google Scholar 

  • Loveland, T. R., Sohl, T. L., Stehman, S. V., Gallant, A. L., Sayler, K. L., & Napton, D. E. (2002). A strategy for estimating the rates of recent United States land-cover changes. Photogrammetry Engineering & Remote Sensing, 68(10), 1091–1099.

    Google Scholar 

  • Maoguo, G., Yu, L., Licheng, J., Meng, J., & Linzhi, S. (2014). SAR change detection based on intensity and texture changes. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 123–135.

    Article  Google Scholar 

  • Menges, C. M., Hadley, D. G., Woodward, D. G., & Mohamed, F. A. (1993). Geology and geophysics, groundwater resources of Al-Ain Area, Abu Dhabi Emirate. In: US Geological Survey Administration Rep 93-001 (pp. 74–167).

  • Michalek, J. L., Wagner, T. W., Luczkovich, J. J., & Stoffle, R. W. (1993). Multispectral change vector analysis for monitoring coastal marine environments. Photogrammetry Engineering & Remote Sensing, 59(3), 381–384.

    Google Scholar 

  • Robb, D. M., & Russell, G. C. (1998). A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering & Remote Sensing, 64(3), 207–216.

    Google Scholar 

  • Samy I.E., & Mohamed, M. M. (2014). Probabilistic frequency ratio model for groundwater potential map** in Al Jaww plain, UAE. Arabian Journal of Geosciences, 1–12.

  • Samy, I. E., & Mohamed, M. M. (2012). Topographic attributes control groundwater flow and groundwater salinity of Al Ain, UAE: a prediction method using remote sensing and GIS. Journal of Environment and Earth Science, 2(8).

  • Schenk, T., & Csatho, B. (2012). A new methodology for detecting ice sheet surface elevation changes from laser altimetry data. Geoscience and Remote Sensing, IEEE Transactions on, 50(9), 3302–3316.

  • Schmitt, A., & Brisco, B. (2013). Wetland monitoring using the curvelet-based change detection method on polarimetric SAR imagery. Water, 5, 1036–1051.

    Article  Google Scholar 

  • Sohl, T. (1999). Change analysis in the United Arab Emirates: an investigation of techniques. Photogrammetric Engineering and Remote Sensing, 65, 475–484.

    Google Scholar 

  • Srivastava, P. K., Han, D., Ramirez, M. R., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50, 1250–1265.

    Article  Google Scholar 

  • Veettil, B. K. (2012). A comparative study of urban change detection techniques using high spatial resolution images. Proceedings of the 4th Geobia, May 7–9, 2012 - Rio De Janeiro - Brazil. p. 029.

  • Yagoub M. M., & Kolan, G. R. (2006) Monitoring coastal zone land use and land cover changes of Abu Dhabi using remote sensing. Journal of the Indian Society of Remote Sensing, 34(1).

  • Yagoup, M. M. (2004). Monitoring of urban growth of a Desert City through remote sensing: Al-Ain, Uae, between 1976 and 2000. International Journal of Remote Sensing, 25(6), 1063–1076.

    Article  Google Scholar 

  • Yang, X., & Lo, C. P. (2002). Using a time series of satellite imagery to detect land use and land cover changes in Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 9, 1775–1798.

    Article  Google Scholar 

  • Yang, Y., Zhou, Q., Gong, J., & Wang, Y. (2012). An integrated spatio-temporal classification method for urban fringe change-detection analysis. International Journal of Remote Sensing, 33, 2516–2531.

    Article  Google Scholar 

  • Zhan, X., Sohlberg, R., Townshend, J. R., Dimiceli, C., Carroll, M. L., Eastman, J. C., Hansen, M. C., & Defries, R. S. (2002). Detection of land cover changes using Modis 250 M data. Remote Sensing of Environment, 83(2), 336–350.

    Article  Google Scholar 

  • Zhang, L., Liao, M., Yang, L., & Lin, H. (2007). Remote sensing change detection based on canonical correlation analysis and contextual Bayes decision. Photogrammetric Engineering & Remote Sensing, 73(3), 311–318.

    Article  Google Scholar 

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Authors and Affiliations

  1. Civil and Environmental Engineering Department, United Arab Emirates University, P.O. Box 15551, Al-Ain, United Arab Emirates

    Samy Ismail Elmahdy & Mohamed Mostafa Mohamed

  2. Irrigation and Hydraulics Department, Faculty of Engineering, Cairo University, P.O. Box 12211, Giza, Egypt

    Mohamed Mostafa Mohamed

Authors
  1. Samy Ismail Elmahdy
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  2. Mohamed Mostafa Mohamed
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Correspondence to Samy Ismail Elmahdy.

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Elmahdy, S.I., Mohamed, M.M. Automatic Feature Extraction Module for Change Detection in Al Ain, UAE: Analysis by Means of Multi-temporal Remote Sensing Data. J Indian Soc Remote Sens 44, 1–10 (2016). https://doi.org/10.1007/s12524-015-0448-2

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  • DOI: https://doi.org/10.1007/s12524-015-0448-2

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